Machine Learning Course Syllabus - 2024

Watch this video on Machine Learning by Intellipaat

So let’s go ahead.

What is Machine Learning?

Machine learning is a branch of computer science that uses algorithms to imitate the way in which humans learn. It uses statistical methods to train algorithms and make predictions. The accuracy of these predictions improves over time.

As the amount of data increases and big data continues to grow, the demand for data scientists increases along with. Machine learning is one of the most in-demand Data Science skills, which allows data scientists to increase the accuracy of predictions of software applications, without explicitly programming them to do so.

These algorithms make use of historical data to predict output values and these insights and predictions enable businesses to make smart decisions.

Machine Learning is very important as it gives companies a view of the trends in business patterns and customer behaviour. Most leading companies like Uber, Google, and Facebook focus on Machine Learning as the main focus of their operations. 

There are multiple courses you can study to pursue a career in Machine Learning. So now let’s learn about them.

Machine Learning Courses

Machine Learning is one of the fastest-growing fields in the Computer Science industry. These days, every student wants to enhance their skills, the scope of the machine learning field as it has proven to be very beneficial in increasing the placement chances of candidates.

Here are some Machine Learning courses that you can pursue.

Other than these courses mentioned above, many BTech and MTech courses offered in Computer Science also combine many Machine Learning subjects in their curriculum.

Now that you have seen what the different courses in Machine Learning are, let’s learn about the common subjects in the Machine Learning Course Syllabus for these courses.

Important Subjects in Machine Learning Courses

The machine learning courses that we discussed are offered in various streams, countries, and institutes. The exact syllabus will always differ, based on the course you’re pursuing and the college or university you’re studying in, but each one of these courses focuses on the same common subjects

These subjects are designed in a way that they give an overview of Machine Learning.

Some of these subjects are-

• Programming Languages like R, Python, C++, and Java.
• Machine Learning Algorithms and Techniques
• Relation between Artificial Intelligence and Machine Learning
• Artificial Neural Networks and their applications
• Reinforcement Learning and Deep Learning
• Natural Language Processing

All these subjects are usually included in every machine learning course syllabus, be it any level of education from any university or country.

Most of these courses also include mandatory internships and live Machine Leaning projects, during the course. These help the students to learn and understand better and get a better grasp of the subjects being taught.

Machine Learning Course Syllabus: Certifications

To understand the Machine Learning Course Syllabus for Specialization Certifications, let’s look at the syllabus of the machine learning course offered by Intellipaat.

• Module 1 – Introduction to Machine Learning
• Module 2 – Supervised Learning and Linear Regression
• Module 3 – Classification and Logistic Regression
• Module 4 – Decision Tree and Random Forest
• Module 5 – Naïve Bayes and Support Vector Machine
• Module 6 – Unsupervised Learning
• Module 7 – Natural Language Processing and Text Mining
• Module 8 – Introduction to Deep Learning
• Module 9 – Time Series Analysis

The cost of such training and certifications can vary, depending on the course, the offering body, and the quality of the training, and the experience of the faculty, but the fees can usually range between ₹5000 to ₹20000.

Next, let’s come to the undergraduate courses and discuss them next. 

Machine Learning Course Syllabus: Undergraduate

Here are some undergraduate courses in Machine Learning, that you can pursue.

UG Certification in Machine Learning Course Syllabus

After looking at the course syllabi of some UG Certifications in Machine Learning, we could conclude that the Machine Learning Course Syllabus for any UG Certification usually follows a similar pattern, with some changes here and there, depending on the institute. Let’s understand that.

These courses are usually offered online by many reputed colleges, universities, and organizations, including highly prestigious IITs like IIT Madras.

Bachelor’s Degree in Machine Learning Course Syllabus

You can pursue a Bachelor’s degree in either a 6-semester-long course like Computer Science or an 8-semester-long course in Engineering or Technology, with a specialization in Machine Learning.

If you’re pursuing an 8-semester-long course, you might study some additional subjects like Human-Computer Interaction, Data Mining, Data Visualization, Data Modelling, Pattern Recognition, and Augmented Reality.

Machine Learning Course Syllabus: Post-Graduate

Here are some Post-Graduation courses that you can pursue.

PG Certification in Machine Learning Course Syllabus

Let’s look at the syllabus of the PG Certification in Machine Learning offered by Intellipaat, to understand the topics that are covered in the Machine Learning Course Syllabus for PG Certifications. 

• Module 1 – Preparatory Classes on Python for AI & ML and Linux
• Module 2 – Git and GitHub
• Module 3 – Python with Data Science
• Module 4 – Data Wrangling with SQL
• Module 5 – Story Telling
• Module 6 – Machine Learning Models for Selection and Tuning
• Module 7 – Machine Learning & Prediction Algorithms
• Module 8 – Advanced Machine Learning
• Module 9 – Software Engineering for Data Science
• Module 10 – Data Science at Scale with PySpark
• Module 11 – Artificial Intelligence and Deep Learning with TensorFlow
• Module 12 – Natural Language Processing
• Module 13 – Image Processing and Computer Vision
• Module 14 – Deployment of Machine Learning Systems to Production
• Module 15 – Work with Large Datasets
• Module 16 – Data Visualization with Tableau
• Module 17 – Capstone Project
• Module 18 – Data Science with R

Master’s Degree in Machine Learning Course Syllabus

After completing your undergraduate, you are eligible to pursue a 2-year-long Master’s program in Machine Learning. We analyzed the Machine Learning Course Syllabus for Master’s Programs in various reputed universities and concluded that the students usually have to study the following core and elective subjects.

• Core Subjects
  • Introduction to Machine Learning
  • Deep Learning or Deep Reinforcement Learning
  • Probabilistic Graphical Models
  • Machine Learning in Practice
  • Convex Optimization
  • Probability & Mathematical Statistics
• Elective Subjects
  • Advanced Deep Learning
  • Advanced Machine Learning: Theory and Methods
  • Machine Learning with Large Datasets
  • Algorithms for NLP
  • Machine Learning for Text Mining
  • Neural Networks for NLP
  • Multimodal Machine Learning
  • Algorithms
  • Graduate Artificial Intelligence
  • Multimedia Databases and Data Mining
  • Algorithms in the Real World
  • Computer Vision and Imaging
  • Regression Analysis
  • Advanced Statistical Theory
  • Algorithms and Complexity
  • Intelligent Robotics
  • Machine Learning and Intelligent Data Analysis
  • Neural Computation
  • Robot Vision

Book recommendations for Machine Learning Course Syllabus

Bachelor’s Degree

Given below is a list of books that may prove to be useful to students pursuing a Bachelor’s Degree in Machine Learning.

Master’s Degree

The following books will be useful if you’re pursuing a Master’s Degree in Machine Learning.

Now, hopefully, you’re familiar with the Machine Learning Course Syllabus at all the different levels.